Socioeconomic inequalities of 3-year survival in formal employees with colorectal cancer between 2012 and 2019 in Colombia

Socioeconomic inequalities of 3-year survival in formal employees with colorectal cancer between 2012 and 2019 in Colombia | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Socioeconomic inequalities of 3-year survival in formal employees with colorectal cancer between 2012 and 2019 in Colombia View ORCID Profile Daniela Sánchez-Santiesteban , Andrés Felipe Patiño-Benavidez , Giancarlo Buitrago doi: https://doi.org/10.1101/2024.12.07.24318651 Daniela Sánchez-Santiesteban 1 Instituto de Investigaciones Clínicas, Facultad de Medicina, Universidad Nacional de Colombia , Bogotá D.C., Colombia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Daniela Sánchez-Santiesteban For correspondence: dansanchezsa{at}unal.edu.co Andrés Felipe Patiño-Benavidez 1 Instituto de Investigaciones Clínicas, Facultad de Medicina, Universidad Nacional de Colombia , Bogotá D.C., Colombia 2 Hospital Universitario Nacional de Colombia , Bogotá D.C., Colombia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Giancarlo Buitrago 1 Instituto de Investigaciones Clínicas, Facultad de Medicina, Universidad Nacional de Colombia , Bogotá D.C., Colombia 3 Fundación Cardioinfantil – La Cardio , Bogotá D.C., Colombia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Objective This study aimed to assess socioeconomic inequalities in three-year survival among formal employees diagnosed with colorectal cancer in Colombia between 2012 and 2019, using legal monthly minimum wages (MMW) as a proxy for socioeconomic status. Methods A retrospective cohort study used administrative databases, including healthcare and mortality records. Formal employees newly diagnosed with colorectal cancer were identified through diagnostic and oncological procedure codes and followed for three years from diagnosis or until death. Patients were stratified into MMW quartiles, and Cox regression models were employed to estimate adjusted hazard ratios (HRs) for survival. Socioeconomic gradients were quantified using the Relative Index of Inequality (RII) and Slope Index of Inequality (SII). Results The cohort included 1,913 formal employees (mean age: 49.9 years), with 660 deaths (34.5%) recorded over the follow-up period. Patients in the lowest MMW quartile experienced the highest three-year mortality (39.5%) compared to those in the highest quartile (30.7%). After adjusting for confounders, individuals in the highest quartile had a 25% lower risk of death than those in the lowest quartile (aHR: 0.74; 95% CI: 0.59–0.92). The RII indicated a 50% higher risk of death in the lowest income group (RII: 1.50; 95% CI: 1.13–1.99), while the SII revealed an absolute difference of 0.16 deaths per 1,000 individuals (p=0.01). Conclusion Significant income-based disparities in colorectal cancer survival were observed among formal employees in Colombia despite the theoretically equitable healthcare system. These findings underscore the persistent influence of socioeconomic factors on health outcomes, even within populations assumed to have better access to care. Introduction Colorectal cancer is one of the leading causes of cancer-related morbidity and mortality worldwide. In 2020, it ranked as the second most deadly cancer and the third most commonly diagnosed, with an estimated 1.9 million new cases and 935,000 deaths annually [ 1 ]. Projections indicate that by 2040, the global burden could rise to 3.2 million new cases and 1.6 million deaths yearly [ 2 ]. In Colombia, colorectal cancer was the third leading cause of cancer incidence and mortality in 2021, primarily affecting individuals aged 50 and older, many of whom belong to the formal workforce [ 3 ]. In low- and middle-income countries (LMICs) like Colombia, formal employees are often regarded as a relatively advantaged group due to their regular income and access to employer-provided health benefits [ 4 ]. However, this perceived advantage does not entirely shield them from significant barriers to adequate healthcare access, particularly for complex conditions such as colorectal cancer. This disease requires timely and multidisciplinary care, which can be hindered by structural and socioeconomic factors, even among populations with formal employment and health coverage [ 5 , 6 ]. The Colombian health system, comprising contributory and subsidised schemes, covers over 97% of the population and aims to ensure equitable healthcare access regardless of affiliation [ 7 ]. Additionally, clinical practice guidelines have been developed to standardise diagnosing and managing diseases like colorectal cancer. Despite these measures, evidence from Colombia and other settings reveals persistent socioeconomic disparities in early detection, timely treatment, and survival outcomes, even within systems designed to promote equity [ 8 - 10 ]. Studying survival disparities among formal employees with colorectal cancer provides a unique perspective, as this group is critical to national productivity and is presumed to face fewer healthcare access barriers compared to informal workers or the unemployed. Nonetheless, fragmented care and delays in accessing specialised services remain significant challenges that may contribute to health outcomes inequity [ 11 ]. This study aims to assess socioeconomic inequalities in three-year survival among formal employees diagnosed with colorectal cancer in Colombia from 2012 to 2019, using monthly minimum wages (MMW) as a measure of socioeconomic status. By focusing on a population assumed to have relatively uniform access to healthcare, this analysis highlights the persistent influence of income disparities on health outcomes. It contributes to the broader understanding of social determinants of health. Methods Data source and study population This retrospective cohort study analysed administrative databases to examine the entire population of formal employees in Colombia who were newly diagnosed and treated for colorectal cancer between January 1, 2012, and December 31, 2019. The cohort was constructed using the Base for the Study of Capitation Unit Sufficiency (UPC) to identify patients meeting criteria established by an electronic algorithm based on diagnostic and oncological procedure codes. Since ICD-10 codes in the UPC database may indicate confirmed or suspected diagnoses, electronic algorithms are essential to identify individuals with specific conditions accurately. Colorectal cancer cases were defined as patients with at least three diagnostic codes recorded in different months and at least one oncological procedure code (e.g., chemotherapy, radiotherapy, or surgery). This algorithm has been validated and used in previous studies [ 14 , 16 , 17 ]. The list of ICD-10 and CUPS codes used in the algorithm is available in the S1 File. The final study sample included all adults who met the colorectal cancer diagnosis algorithm criteria during the study period and were actively employed at the time of diagnosis. Cohort entry was defined as the date of the first recorded ICD-10 code, with follow-up extending for three years or until death, whichever occurred first. Data was accessed on August 10, 2024. This study was approved by the Faculty of Medicine Ethics Committee at the Universidad Nacional de Colombia (Approval Number: 019-179). This study utilised four primary administrative databases, anonymised and linked using unique identifiers by the Ministry of Health (MoH). The UPC includes comprehensive records of healthcare service utilisation for individuals within the health system, capturing information on the type of services provided, associated ICD-10 codes, costs, service dates, municipalities, and healthcare providers. This database supports risk-adjusted capitated payment calculations and is populated by the largest insurers within the contributory scheme, covering approximately 19 million individuals (88% of this scheme’s population nationwide). It is highly standardised and reports minimal underreporting of health service utilisation [ 12 ]. The Unique Enrollees Database (BDUA) is the government registry for tracking demographic and enrolment information of all health system beneficiaries across contributory, subsidised, and other schemes. It provides data on enrolment status, age, sex, insurer, and affiliation scheme [ 18 ]. The Death Registry (RUAF) compiles mortality data from death certificates, including location, date, cause of death, and sociodemographic characteristics (e.g., sex, age, marital status, education level, occupation, and ethnicity). International assessments have confirmed the reliability of RUAF data, with 91% of deaths registered through death certificates as of 2016 [ 13 , 19 ]. Finally, the Wages Database (PILA), managed by the MoH, contains detailed records on payroll contributions to health, pensions, and social security and precise information on formal employee wages. This database enables analysis of employment trends and wage distribution. While self-employed individuals self-report income, formal employee wage data is aggregated and reported by employers, ensuring higher data quality for this group [ 20 ]. These datasets, widely used in national studies [ 14 , 16 , 21 – 24 ], were provided to the Clinical Research Institute of the Universidad Nacional de Colombia for this research. Study variables This study examined socioeconomic inequalities in 3-year survival among colorectal cancer patients. The primary exposure variable was the patient’s socioeconomic position, measured as the formal wage earned during cohort entry, as recorded in the PILA database. This variable was expressed in units of legal monthly minimum wages (MMW) and further stratified into quartiles to facilitate analysis. The primary outcome variable was 3-year survival time, defined as the period from the cohort entry date to the date of death or the end of the 3-year follow-up period, whichever occurred first. The date of death was obtained from death certificates in the RUAF database. We included sociodemographic and clinical variables measured during colorectal cancer diagnosis to control for potential confounders. Sociodemographic variables included age, sex, insurer, and geographic region (Colombia is divided into 32 departments). Clinical variables included the Charlson Comorbidity Index (CCI), validated for use in Colombia by Oliveros et al. [ 24 ], derived from ICD-10 codes. The CCI considered the presence of the following conditions: acute myocardial infarction, congestive heart failure, peripheral vascular disease, stroke, dementia, chronic pulmonary disease, connective tissue disease, peptic ulcer disease, liver disease, diabetes, complications of diabetes, cancer, metastatic cancer, paraplegia, renal disease, severe liver disease, and HIV. Based on the oncological services used during diagnosis and treatment, we classified the cancer stage as local, locally advanced, or advanced. Statistical analysis We performed descriptive statistics on the sociodemographic characteristics of formal employees with colorectal cancer. Continuous variables with normal distributions were summarised using means and standard deviations, while continuous variables with non-normal distributions were summarised using medians and interquartile ranges. Categorical variables were summarised using absolute and relative frequencies. The descriptive analysis was stratified according to quartiles of wages. We described the three-year mortality proportion by quartiles of wages with 95% confidence intervals. Using semiparametric Cox regression models, we estimated unadjusted and adjusted hazard ratios (HRs) and 95% CIs for three-year survival. Variables used for model adjustment included sex, age, year, insurer, geographic region, CCI, cancer stage, and year of diagnosis. Statistical and graphical tests were performed to evaluate the assumption of proportionality. We estimated the Relative Index of Inequality (RII) and the Slope Index of Inequality (SII) to quantify the socioeconomic gradient in absolute and relative terms. As these indices can only be estimated for socioeconomic characteristics with at least three categories, they were identified for the quartile of MMW at the month of first diagnosis of colorectal cancer. Following the recommendations of Moreno-Betancur et al., we used Poisson regression models to estimate the RII as the incidence rate ratio between the individual with the best socioeconomic position and the one with the worst. After conducting the Poisson regression, the SII was calculated from the absolute difference in the predicted mortality rates per 1,000 adults for the two groups. All indices were adjusted with the variables previously mentioned in the survival models. All analyses were conducted using Stata v.18 MP (Universidad Nacional de Colombia license). This article followed the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines to ensure transparency and thoroughness in reporting the findings. Additionally, an artificial intelligence language model, specifically OpenAI’s GPT-4, assisted with style corrections and enhanced the text’s clarity and coherence during the writing process. Results Between January 1, 2012, and December 31, 2019, 1,950 formal employees were diagnosed with colorectal cancer. After excluding 37 records (1.89%) due to incomplete data on variables such as age and sex, the final cohort consisted of 1,913 individuals who were followed for three years or until death, whichever occurred first. The flowchart in Figure 1 provides additional details on the selection process. Among the final cohort, 1,164 individuals (60.85%) were male, and the mean age at diagnosis was 49.88 years (SD: 10.58). The majority of patients (1,231; 64.35%) were diagnosed with locally advanced colorectal cancer, and 76.48% had a CCI of 2 at the time of diagnosis. Most cases were concentrated in Bogotá (623 cases; 32.57%) and the Central region (558 cases; 29.17%). Download figure Open in new tab Fig 1. STROBE flow chart. UPC: Base for the Study of Capitation Unit Sufficiency Database; PILA: Wages Database Table 1 presents detailed sociodemographic and clinical characteristics of the cohort. The distribution of monthly minimum wages (MMW) was stratified into quartiles, with the 25th, 50th, and 75th percentiles corresponding to 1.00, 1.31, and 2.34, respectively. MMW values ranged from 1.00 to 24.81. Median MMWs varied significantly across regions, with the Orinoquía y Amazonías region reporting the highest median MMW (5.63) and the Atlántica region the lowest (1.24). View this table: View inline View popup Table 1. Baseline characteristics A total of 660 deaths were observed during the follow-up period, corresponding to a three-year mortality proportion of 34.50%. The regions with the highest mortality rates were Atlántica (41.14%) and Pacífica (39.93%), exceeding the overall cohort mortality proportion. Mortality proportions stratified by MMW quartiles revealed significant differences. Patients in the lowest MMW quartile had the highest three-year mortality (39.47%; 95% CI: 35.22–43.88), while those in the highest quartile had the lowest (30.67%; 95% CI: 26.15–35.72). Figure 2 illustrates the three-year mortality rates across MMW quartiles, showing a transparent socioeconomic gradient. Download figure Open in new tab Fig 2. Three-year mortality proportion of formal employees with colorectal cancer per quartile of MMW. MMW: Monthly Minimum Wages The risk of death within three years of a colorectal cancer diagnosis significantly increased with lower MMW, as shown in both crude and adjusted hazard ratios. After adjusting for age, sex, cancer stage, CCI, region, insurer, and year of diagnosis, formal employees in the highest MMW quartile had a 25% lower risk of death compared to those in the lowest quartile (adjusted HR: 0.74; 95% CI: 0.59–0.92). Table 2 summarises the unadjusted and adjusted HRs for each MMW quartile. At the same time, Figure 3 displays survival curves from the multivariate models, highlighting a dose-response relationship: lower MMWs were associated with reduced survival. Download figure Open in new tab Fig 3. Overall three-year survival of formal employees with colorectal cancer per quartile of MMW. MMW: Monthly Minimum Wages. View this table: View inline View popup Download powerpoint Table 2. Unadjusted and Adjusted HR of overall survival of formal employees with colorectal cancer per quartile of MMW The calculated RII and SII further supported these findings, showing significant differences by MMW even after adjustment for key covariates ( Table 3 ). View this table: View inline View popup Table 3. Relative and Slope Index of inequality (RII & SII) in overall mortality of patients with colorectal cancer belonging to the contributory regime in Colombia by MMW Discussion This population-based study focused on formal employees diagnosed with colorectal cancer in a low- and middle-income country (LMIC), specifically Colombia. Over seven years, patients were followed for three years to assess survival outcomes. To our knowledge, this is the first study in an LMIC targeting a theoretically advantaged population due to their formal employment status and the associated health benefits. Our findings reveal significant disparities in three-year survival rates based on income, even within this ostensibly protected group. Despite being part of a health system designed to ensure equitable access to healthcare services, our results underscore that income remains a critical determinant of survival, challenging the assumption of universal access and highlighting persistent inequalities in health outcomes. These findings align with research conducted in both high-income countries and LMICs, where substantial disparities in cancer survival related to socioeconomic status have been documented, even in systems striving for equitable healthcare access [ 25 ]. Numerous studies have demonstrated the impact of income inequalities on colorectal cancer survival, consistently reporting lower survival rates among individuals with lower incomes [ 26 – 30 ]. Disparities have also been identified across other dimensions, including geographic location, residential socioeconomic status, education level, race, and health insurance coverage [ 9 , 31 – 34 ]. Our findings are further supported by studies evaluating income-based inequalities using indicators such as the SII and the RII, which consistently show significant differences in outcomes based on these factors [ 35 , 36 ]. This study contributes to this growing body of evidence by highlighting that income plays a crucial role in survival outcomes, even within a relatively advantaged group, such as formal employees, who theoretically face fewer healthcare access barriers. It is important to note that income disparities often intersect with other forms of inequality, such as differences in education, working conditions, geographic barriers, and the quality of healthcare services. For example, lower-income individuals may reside in areas with limited access to specialised healthcare or face longer wait times for advanced treatments, even in systems that aim to provide equitable healthcare access. These interconnected factors likely contribute to the observed disparities in survival outcomes, even in settings where primary healthcare access is ostensibly uniform. Beyond access to specialised care, other structural barriers—such as the quality and continuity of care, navigation through the healthcare system, and the availability of multidisciplinary teams—may also significantly influence survival outcomes. This study has several strengths. The reliability of the data is a crucial advantage, as it includes comprehensive information on income and formal employee status, with wage data mandatorily reported by employers to the PILA database under national regulations. Furthermore, the RUAF death registry provides extensive coverage, documenting over 90% of deaths nationwide, making it a robust resource for investigating healthcare inequalities. These databases, including the UPC and BDUA, have been widely used in previous studies, further supporting their validity as valuable tools for research. However, some limitations should be considered. Patient identification relied on algorithms using procedural and ICD-10 codes, which, while necessary for administrative databases, could lead to misclassification. Additionally, although administrative databases provide reliable data on healthcare utilisation, they lack detailed clinical variables that could offer a more nuanced understanding of cancer characteristics. To mitigate this limitation, we staged the disease based on procedure codes related to cancer treatment, supported by clinical expertise. Finally, this study focuses on a relatively privileged population in terms of health status and income. The income-related disparities identified here may be even more pronounced in other population groups, such as informal employees or the unemployed. Moreover, survival differences could be exacerbated in countries with greater overall inequality. Thus, while our findings are significant, they may underestimate the true extent of socioeconomic disparities in cancer outcomes across broader or more disadvantaged populations. Further studies are needed to explore the impact of social inequalities on health outcomes in populations with poorer socioeconomic conditions and more significant barriers to adequate healthcare access. Data Availability The following information sources: Single Registry of Enrollees (Registro Único de Afiliación, or RUAF), Unique Affiliation Database (Base de Datos Única de Afiliación, or BDUA), Wages Database (PILA), and Calculation Study of the Capitation Unit Database (Base del Estudio de Suficiencia de la Unidad Por Capitación, or UPC) are administered by the Colombian Ministry of Health and Social Protection. These databases are freely available upon request to the Technology of the Information and Communication Office of the Colombian Ministry of Health and Social Protection through the e-mail: correo{at}minsalud.gov.co . Author Contributions Conceptualization : Giancarlo Buitrago. Data curation : Giancarlo Buitrago, Andrés Felipe Patiño-Benavidez. Formal analysis: Daniela Sánchez-Santiesteban, Andrés Felipe Patiño-Benavidez, Giancarlo Buitrago. Methodology: Daniela Sánchez-Santiesteban, Andrés Felipe Patiño-Benavidez, Giancarlo Buitrago. Software: Daniela Sánchez-Santiesteban, Andrés Felipe Patiño-Benavidez, Giancarlo Buitrago. Writing – original draft: Daniela Sánchez-Santiesteban, Andrés Felipe Patiño-Benavidez, Giancarlo Buitrago. Writing – review & editing: Daniela Sánchez-Santiesteban, Andrés Felipe Patiño-Benavidez, Giancarlo Buitrago. Supporting information S1 File. List of ICD-10 and CUPS codes is used in the electronic algorithm for colorectal cancer. S1 Table. Coefficients of survival regression. Acknowledgments We thank Colombia’s Ministry of Health and Social Protection for providing the administrative databases that made this study possible. We also thank the Clinical Research Student Group at the Faculty of Medicine of the National University of Colombia for their valuable contributions. This study’s preliminary findings were presented and discussed within this group, enriching the interpretation and scope of our results. The School of Medicine at Universidad Nacional de Colombia supported the study. Additionally, this research was partially funded by the NIHR GHPSR researcher-led grant NIHR150067, which used UK aid from the UK Government to support global health research. References 1. ↵ Sung H , Ferlay J , Siegel RL , Laversanne M , Soerjomataram I , Jemal A , et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries . CA Cancer J Clin . 2021 ; 71 : 209 – 249 . doi: 10.3322/caac.21660 OpenUrl CrossRef PubMed 2. ↵ Morgan E , Arnold M , Gini A , Lorenzoni V , Cabasag CJ , Laversanne M , et al. Global burden of colorectal cancer in 2020 and 2040: incidence and mortality estimates from GLOBOCAN . Gut . 2023 ; 72 : 338 – 344 . doi: 10.1136/gutjnl-2022-327736 OpenUrl Abstract / FREE Full Text 3. ↵ Márquez-Ustariz A , Guerrero-Macías S , Burgos-Sánchez R , Campillo-Pardo J , Bustos-Guerrero A , García-Mora M , et al. Updates in colorectal cancer screening . 2023 ; 27 : 291 – 300 . doi: 10.35509/01239015.931 OpenUrl CrossRef 4. ↵ Torm N , Oehme M . Social protection and formalization in low- and middle-income countries: A scoping review of the literature . World Dev . 2024 ; 181 : 106662 . doi: 10.1016/j.worlddev.2024.106662 OpenUrl CrossRef 5. ↵ Goetzel RZ , Long SR , Ozminkowski RJ , Hawkins K , Wang S , Lynch W. Health, Absence, Disability, and Presenteeism Cost Estimates of Certain Physical and Mental Health Conditions Affecting U . S. Employers: J Occup Environ Med . 2004 ; 46 : 398 – 412 . doi: 10.1097/01.jom.0000121151.40413.bd OpenUrl CrossRef PubMed Web of Science 6. ↵ Ministerio de Salud y Protección Social . Comportamiento del aseguramiento . [cited 21 Aug 2024 ]. Available: https://www.minsalud.gov.co/proteccionsocial/Regimensubsidiado/Paginas/coberturas-del-regimen-subsidiado.aspx 7. ↵ Dirección de Regulación de la Operación del Aseguramiento en Salud, Riesgos Laborales y Pensiones . Boletín del aseguramiento en salud . 2017 . Available: https://www.minsalud.gov.co/sites/rid/Lists/BibliotecaDigital/RIDE/VP/DOA/boletin-aseguramiento-i-trimestre-2017.pdf 8. ↵ Nwankwo EC , Hendrix C , Pollard K , Kallal C , Cruschiel T , Buckhold F , et al. Epidemiologic disparities in colon cancer screening and adherence during the COVID-19 pandemic: a retrospective cohort analysis . Int J Colorectal Dis . 2022 ; 37 : 849 – 854 . doi: 10.1007/s00384-022-04118-6 OpenUrl CrossRef PubMed 9. ↵ Wang C , Zheng Y , Luo Z , Xie J , Chen X , Zhao L , et al. Socioeconomic characteristics, cancer mortality, and universal health coverage: A global analysis . Med . 2024 ; 5 : 926 – 942 .e3. doi: 10.1016/j.medj.2024.04.002 OpenUrl CrossRef PubMed 10. ↵ Albawaliz A , Zafar Y , Kaur A , Madhusudhana S , Shrestha A . Do socioeconomic factors really have an impact on early detection of hepatocellular carcinoma and colorectal cancer? J Clin Oncol . 2020 ; 38 : 60 – 60 . doi: 10.1200/JCO.2020.38.4_suppl.60 OpenUrl CrossRef 11. ↵ Organización Panamericana de la Salud . Health in the Americas . Washington D.C. ; 2007 . Available: https://www.paho.org/hq/dmdocuments/2010/Health_in_the_Americas_Vol_1_Regional_2007.pdf 12. ↵ Ministerio de Salud y Protección Social . Unidad de pago por capitación (UPC) . [cited 25 May 2023 ]. Available: https://www.minsalud.gov.co/salud/POS/Paginas/unidad-de-pago-por-capitacion-upc.aspx 13. ↵ Ministerio de Salud y Protección Social . Registro Único de Afiliados RUAF . [cited 25 Apr 2023 ]. Available: https://ruaf.sispro.gov.co/ 14. ↵ Gamboa Ó , Buitrago G , Patiño AF , Agudelo NR , Espinel LS , Eslava-Schmalbach J , et al. Fragmentation of Care and Its Association With Survival and Costs for Patients With Breast Cancer in Colombia . JCO Glob Oncol . 2023 ; e2200393 . doi: 10.1200/GO.22.00393 OpenUrl CrossRef 15. Patiño-Benavidez AF , Buitrago G , Rozo-Agudelo N , Saldaña-Espinel LE , Gamboa-Garay ÓA , Eslava-Schmalbach J , et al. Association of Healthcare Fragmentation and the Survival of Patients with Colorectal Cancer in Colombia . Value Health Reg Issues . 2024 ; 41 : 63 – 71 . doi: 10.1016/j.vhri.2023.12.003 OpenUrl CrossRef PubMed 16. ↵ Saldaña L , Patiño A , Rozo N , Gamboa O , Paneso JE , Bernal M , et al. Estimating breast, stomach, and colorectal cancer incidence in Colombia through administrative database algorithms: A systematic review of literature and real-world data study . 17. ↵ Patiño AF , Buitrago G , Agudelo NR , Espinel LS , Gamboa O , Eslava-Schmalbach J , et al. Association of healthcare fragmentation and the survival of patients with colorectal cancer in Colombia . In Review ; 2022 Aug . doi: 10.21203/rs.3.rs-1856519/v1 OpenUrl CrossRef 18. ↵ Fondo de Pensiones Públicas del Nivel Nacional . ¿Qué es la BDUA? [cited 25 May 2023 ]. Available: https://www.fopep.gov.co/preguntas_frecuentes/que-es-la-bdua/ 19. ↵ Cendales R , Pardo C . Quality of death certification in Colombia . Colomb Médica CM . 49 : 121 – 127 . doi: 10.25100/cm.v49i1.3155 OpenUrl CrossRef 20. ↵ Ministerio de Salud y Protección Social . Planilla Integrada de Liquidación de Aportes . [cited 25 Apr 2023 ]. Available: https://www.minsalud.gov.co/proteccionsocial/Paginas/pila.aspx 21. ↵ Buitrago G , Moreno-Serra R . Conflict violence reduction and pregnancy outcomes: A regression discontinuity design in Colombia . Stock SJ , editor. PLOS Med . 2021 ; 18 : e1003684 . doi: 10.1371/journal.pmed.1003684 OpenUrl CrossRef PubMed 22. Buitrago G , Arevalo K , Moyano JS , Caycedo R , Gaitan H . Appendectomy in Third Trimester of Pregnancy and Birth Outcomes: A Propensity Score Analysis of a 6-Year Cohort Study Using Administrative Claims Data . World J Surg . 2020 ; 44 : 12 – 20 . doi: 10.1007/s00268-019-05200-x OpenUrl CrossRef PubMed 23. Buitrago G , Eslava-Schmalbach J , Rincon CJ . Prognostic factors associated with clinical and economic outcomes of appendectomies in children: a multilevel analysis in a national retrospective cohort study . World J Surg . 2020 ; 44 : 303 – 312 . OpenUrl PubMed 24. ↵ Oliveros H , Buitrago G . Validation and adaptation of the Charlson Comorbidity Index using administrative data from the Colombian health system: retrospective cohort study . BMJ Open . 2022 ; 12 : e054058 . doi: 10.1136/bmjopen-2021-054058 OpenUrl CrossRef PubMed 25. ↵ Andaya AA , Enewold L , Zahm SH , Shriver CD , Stojadinovic A , McGlynn KA , et al. Race and Colon Cancer Survival in an Equal-Access Health Care System . Cancer Epidemiol Biomarkers Prev . 2013 ; 22 : 1030 – 1036 . doi: 10.1158/1055-9965.EPI-13-0143 OpenUrl Abstract / FREE Full Text 26. ↵ Ferreira MDC , Sarti FM , Barros MBDA . Social inequalities in the incidence, mortality, and survival of neoplasms in women from a municipality in Southeastern Brazil . Cad Saúde Pública . 2022 ; 38 : e00107521 . doi: 10.1590/0102-311x00107521 OpenUrl CrossRef PubMed 27. Le H , Ziogas A , Lipkin SM , Zell JA . Effects of Socioeconomic Status and Treatment Disparities in Colorectal Cancer Survival . Cancer Epidemiol Biomarkers Prev . 2008 ; 17 : 1950 – 1962 . doi: 10.1158/1055-9965.EPI-07-2774 OpenUrl Abstract / FREE Full Text 28. Nitzkorski JR , Willis AI , Nick D , Zhu F , Farma JM , Sigurdson ER . Association of Race and Socioeconomic Status and Outcomes of Patients with Rectal Cancer . Ann Surg Oncol . 2013 ; 20 : 1142 – 1147 . doi: 10.1245/s10434-012-2837-x OpenUrl CrossRef PubMed 29. Feller A , Schmidlin K , Bordoni A , Bouchardy C , Bulliard J , Camey B , et al. Socioeconomic and demographic inequalities in stage at diagnosis and survival among colorectal cancer patients: evidence from a Swiss population-based study . Cancer Med . 2018 ; 7 : 1498 – 1510 . doi: 10.1002/cam4.1385 OpenUrl CrossRef PubMed 30. ↵ Brand NR , Qu LG , Chao A , Ilbawi AM . Delays and Barriers to Cancer Care in Low- and Middle-Income Countries: A Systematic Review . The Oncologist . 2019 ; 24 : e1371 – e1380 . doi: 10.1634/theoncologist.2019-0057 OpenUrl Abstract / FREE Full Text 31. ↵ Carethers JM . Racial and ethnic disparities in colorectal cancer incidence and mortality . Advances in Cancer Research. Elsevier ; 2021 . pp. 197 – 229 . doi: 10.1016/bs.acr.2021.02.007 OpenUrl CrossRef PubMed 32. Mitsakos AT , Irish W , Parikh AA , Snyder RA . The association of health insurance and race with treatment and survival in patients with metastatic colorectal cancer. Patel GK, editor . PLOS ONE . 2022 ; 17 : e0263818 . doi: 10.1371/journal.pone.0263818 OpenUrl CrossRef PubMed 33. Muzi CD , Banegas MP , Guimarães RM . Colorectal cancer disparities in Latin America: Mortality trends 1990–2019 and a paradox association with human development. Haj-Mirzaian A, editor . PLOS ONE . 2023 ; 18 : e0289675 . doi: 10.1371/journal.pone.0289675 OpenUrl CrossRef PubMed 34. ↵ Cramb SM , Mengersen KL , Turrell G , Baade PD . Spatial inequalities in colorectal and breast cancer survival: Premature deaths and associated factors . Health Place . 2012 ; 18 : 1412 – 1421 . doi: 10.1016/j.healthplace.2012.07.006 OpenUrl CrossRef PubMed 35. ↵ Kim C-W , Lee S-Y , Moon O-R . Inequalities in cancer incidence and mortality across income groups and policy implications in South Korea . Public Health . 2008 ; 122 : 229 – 236 . doi: 10.1016/j.puhe.2007.07.003 OpenUrl CrossRef PubMed Web of Science 36. ↵ Van Der Heyden JHA , Schaap MM , Kunst AE , Esnaola S , Borrell C , Cox B , et al. Socioeconomic inequalities in lung cancer mortality in 16 European populations . Lung Cancer . 2009 ; 63 : 322 – 330 . doi: 10.1016/j.lungcan.2008.06.006 OpenUrl CrossRef PubMed Web of Science View the discussion thread. Back to top Previous Next Posted December 09, 2024. Download PDF Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Socioeconomic inequalities of 3-year survival in formal employees with colorectal cancer between 2012 and 2019 in Colombia Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. 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